Mincing Data - Gain insight from data

This site will be a container for all my musings about the analytical pipeline.

For this reason it will be necessary to define the Analytical Pipeline (at least define this pipeline from my point of view). From a general perspective (be aware that this is my perspective)
five activities are necessary to build an analytical pipeline, these steps are

source

ingest

process

store

deliver

The overall goal of an analytical pipeline is to answer an analytical question. To achieve this overall goal, different data sources have to be targeted and their data has to be ingested
into the pipeline and properly processed. During these first steps the data ingested into the pipeline often has to be stored in one or more different data stores, each is used or its special
type of usage, finally the result of the data processing has to be delivered to its users, its audience. Depending on the nature of the question, different types of processing methods and also
different data stores may be used along the flow of the data throughout the pipeline.

I put a direction to these activities, but I also added this direction to spur your critical mind, because

source data is of course also stored somewhere

to target the source can become a challenge in the pipeline

to deliver processed data to the its audience can become a another challenge if you try to deliver to mobile workforce

Successfully ingested data often gets processed more than once to answer different questions, and during its existence (inside an analytical data platform) this data will be transformed into
various shapes, wrangled by different algorithms, and ingested into different "data stores".

For this reason I believe that these activities are tightly related, and the above mentioned sequence of these activities will just aid as a guidance.

I will use blog posts to describe how different activities are combined to answer analytical questions. In most of my upcoming blog posts I will link to different topics from the activities used
in the pipeline. Each activity has its own menu and is by itself representing an essential part in analytical pipeline.

Hopefully this site will help its readers as much as it helps me to focus on each activity always knowing that most of the time more than one activity has to be mastered to find an answer to an
analytical question.

There are a lot of possibilities to visualize your data if you are using Power BI. You can use one of the numerous built-in chart types, use one of the custom build visuals that are available
from the market place called app source, and you also can develop your own visuals using javascript or R. With the release of Power BI
Desktop two new possiblities to visualize ones data are available:

Python (currently Python scripts are in preview and are not supported on the PBI service)

One now can assign the data category "Image Url" to a measure

From a data visualization point of view I'm more excited about the 2nd option, than about the addition of Python to the toolstack.

Assigning the data category "Image Url" to a mearsure opens up new possibilities to create measures that return a simple text string where the text represents an svg image. If you are not
familiar with svg images,

this will get you started: https://en.wikipedia.org/wiki/Scalable_Vector_Graphics

What I love about the usage of svg next to the other options for data visualization is the simplicity with wich svg charts can be used. They do not extensive knowledge of a programming language
like JS, R or Python (I'm pretty sure, that some consider the composing of svg charts also as some kind of programming :-) ). Another great advantage is that they just can be used inside the
Power BI table and matrix visual, without any dependencies to other programming environments.

I have to admit that I'm quite excited about the possibility to use svg charts from inside the table and matrix visual. This easily allows to create microcharts and small multiples.

In this post (it's more than likely that this will not be my last one) I use 2 percentage values to create layered segments of a pie.

From my point of view a slice is one of the ideal forms to represent a percentage (a part of the whole), this is due to the resemblance of an reduced watch dial the accompanying pbix file contains a lot more information

The underlying data is sample data, where project performance is measured by 2 values:

Percent completion and

Time spent

"Percent completion" represents how much tasks have been accomplished and "Time spent" reflects how much time has been spent, here the percentage reflects the actual spent time in comparison to
the planned time. Basically it's better if more tasks are completed in less time. "Red" segments are shown when more time is spend than planned:

I also created an angular version of the Harveyball chart from above. I call this version Harveybox. Here I use the "cost" measure "Time spent" on the left side from the box and the "revenue"
measure "percent completion" on the right side. This leads to a rising line that symbolizes "success" in many societies, whereas a slopy line often symbolizes failure: